Name
Abstract | ||
|---|---|---|
In making practical decisions, agents are expected to comply with ideals of behaviour, or norms. In reality, it may not be possible for an individual, or a team of agents, to be fully compliant—actual behaviour often differs from the ideal. The question we address in this paper is how we can design agents that act in such a way that they select collective strategies to avoid more critical failures (norm violations), and mitigate the effects of violations that do occur. We model the normative requirements of a system through contrary-to-duty obligations and violation severity levels, and propose a novel multi-agent planning mechanism based on Decentralised POMDPs that uses a qualitative reward function to capture levels of compliance: N-Dec-POMDPs. We develop mechanisms for solving this type of multi-agent planning problem and show, through empirical analysis, that joint policies generated are equally as good as those produced through existing methods but with significant reductions in execution time. |
Year | DOI | Venue |
|---|---|---|
2018 | https://doi.org/10.1007/s10458-017-9372-x | Autonomous Agents and Multi-Agent Systems |
Keywords | Field | DocType |
Norms,Multi-agent planning,Dec-POMDPs | Normative,Computer science,Operations research,Norm (social),Execution time,Artificial intelligence,Multi-agent planning,Machine learning | Journal |
Volume | Issue | ISSN |
32 | 1 | 1387-2532 |
Citations | PageRank | References |
0 | 0.34 | 20 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luca Gasparini | 1 | 4 | 2.08 |
| Timothy J. Norman | 2 | 1417 | 140.04 |
| Martin J. Kollingbaum | 3 | 390 | 33.38 |